Yet, the computational cost associated with GP inference remains a major challenge. To fully exploit the rich, state-dependent uncertainty description induced by the posterior covariance of the GP, it should be included in the optimal control problem (OCP) formulation and propagated through the dynamics model over the prediction horizon [1]}.
However, incorporating the covariance propagation into the OCP constitutes a major limitation for real-time implementation of the algorithm on embedded hardware, both in terms of additional optimization variables capturing the state covariance, as well as computationally expensive evaluation and differentiation of the GP posterior covariance.
